Glucose sensor signal stability analysis

ABSTRACT

Disclosed are methods, apparatuses, etc. for glucose sensor signal stability analysis. In certain example embodiments, a series of samples of at least one sensor signal that is responsive to a blood glucose level of a patient may be obtained. Based at least partly on the series of samples, at least one metric may be determined to assess an underlying trend of a change in responsiveness of the at least one sensor signal to the blood glucose level of the patient over time. A reliability of the at least one sensor signal to respond to the blood glucose level of the patient may be assessed based at least partly on the at least one metric assessing an underlying trend. Other example embodiments are disclosed herein.

BACKGROUND

1. Field

Subject matter disclosed herein relates to glucose sensor signalstability analysis including, by way of example but not limitation,analyzing a reliability of a glucose sensor signal by attempting todetect a change in responsiveness of the sensor signal.

2. Information

The pancreas of a normal healthy person produces and releases insulininto the blood stream in response to elevated blood plasma glucoselevels. Beta cells (β-cells), which reside in the pancreas, produce andsecrete insulin into the blood stream as it is needed. If β-cells becomeincapacitated or die, which is a condition known as Type I diabetesmellitus (or in some cases, if β-cells produce insufficient quantitiesof insulin, a condition known as Type II diabetes), then insulin may beprovided to a body from another source to maintain life or health.

Traditionally, because insulin cannot be taken orally, insulin has beeninjected with a syringe. More recently, the use of infusion pump therapyhas been increasing in a number of medical situations, including fordelivering insulin to diabetic individuals. For example, externalinfusion pumps may be worn on a belt, in a pocket, or the like, and theycan deliver insulin into a body via an infusion tube with a percutaneousneedle or a cannula placed in subcutaneous tissue.

As of 1995, less than 5% of the Type I diabetic individuals in theUnited States were using infusion pump therapy. Over time, greater than7% of the more than 900,000 Type I diabetic individuals in the U.S.began using infusion pump therapy. The percentage of Type I diabeticindividuals that use an infusion pump is now growing at a rate of over2% each year. Moreover, the number of Type II diabetic individuals isgrowing at 3% or more per year, and increasing numbers of insulin-usingType II diabetic individuals are also adopting infusion pumps.Physicians have recognized that continuous infusion can provide greatercontrol of a diabetic individual's condition, so they are increasinglyprescribing it for patients.

A closed-loop infusion pump system may include an infusion pump that isautomatically and/or semi-automatically controlled to infuse insulininto a patient. The infusion of insulin may be controlled to occur attimes and/or in amounts that are based, for example, on blood glucosemeasurements obtained from an embedded blood-glucose sensor, e.g., inreal-time. Closed-loop infusion pump systems may also employ thedelivery of glucagon, in addition to the delivery of insulin, forcontrolling blood-glucose and/or insulin levels of a patient (e.g., in ahypoglycemic context). Glucagon delivery may also be based, for example,on blood glucose measurements that are obtained from an embeddedblood-glucose sensor, e.g., in real-time.

SUMMARY

Briefly, example embodiments may relate to methods, systems,apparatuses, and/or articles, etc. for glucose sensor signal reliabilityanalysis. Glucose monitoring systems, including ones that are designedto adjust the glucose levels of a patient and/or to operate continually(e.g., repeatedly, at regular intervals, at least substantiallycontinuously, etc.), may comprise a glucose sensor signal that may beassessed for reliability. More specifically, but by way of example only,reliability assessment(s) on glucose sensor signals may include glucosesensor signal stability assessment(s) to detect an apparent change inresponsiveness of a signal.

In one or more example embodiments, a method may include: obtaining aseries of samples of at least one sensor signal that is responsive to ablood glucose level of a patient; determining, based at least partly onthe series of samples, at least one metric assessing an underlying trendof a change in responsiveness of the at least one sensor signal to theblood glucose level of the patient over time; and assessing areliability of the at least one sensor signal to respond to the bloodglucose level of the patient based at least partly on the at least onemetric assessing an underlying trend.

In at least one example implementation, the method may further include:generating an alert signal responsive to a comparison of the at leastone metric assessing an underlying trend with at least one predeterminedthreshold.

In at least one example implementation, the assessing may include:comparing the at least one metric assessing an underlying trend with atleast a first predetermined threshold and a second predeterminedthreshold. In at least one other example implementation, the assessingmay further include: assessing that the reliability of the at least onesensor signal is in a first state responsive to a comparison of the atleast one metric assessing an underlying trend with the firstpredetermined threshold; assessing that the reliability of the at leastone sensor signal is in a second state responsive to a comparison of theat least one metric assessing an underlying trend with the firstpredetermined threshold and the second predetermined threshold; andassessing that the reliability of the at least one sensor signal is in athird state responsive to a comparison of the at least one metricassessing an underlying trend with the second predetermined threshold.In at least one other example implementation, the assessing may furtherinclude: ascertaining at least one value indicating a severity ofdivergence by the at least one sensor signal from the blood glucoselevel of the patient over time based at least partly on the at least onemetric assessing an underlying trend, the first predetermined threshold,and the second predetermined threshold.

In at least one example implementation, the method may further include:acquiring the at least one sensor signal from one or more subcutaneousglucose sensors, wherein the at least one metric assessing an underlyingtrend may reflect an apparent reliability of the at least one sensorsignal that is acquired from the one or more subcutaneous glucosesensors. In at least one example implementation, the method may furtherinclude: altering an insulin infusion treatment for the patientresponsive at least partly to the assessed reliability of the at leastone sensor signal.

In at least one example implementation, the determining may include:producing the at least one metric assessing an underlying trend using aslope of a linear regression that is derived at least partly from theseries of samples of the at least one sensor signal. In at least oneother example implementation, the method may include: transforming theseries of samples of the at least one sensor signal to derive amonotonic curve, wherein the producing may include calculating the slopeof the linear regression, with the linear regression being derived atleast partly from the monotonic curve.

In at least one example implementation, the determining may include:decomposing the at least one sensor signal as represented by the seriesof samples using at least one empirical mode decomposition and one ormore spline functions to remove relatively higher frequency componentsfrom the at least one sensor signal. In at least one exampleimplementation, the determining may include: decomposing the at leastone sensor signal as represented by the series of samples using at leastone discrete wavelet transform; and reconstructing a smoothed signalfrom one or more approximation coefficients resulting from the at leastone discrete wavelet transform. In at least one example implementation,the determining may include: iteratively updating a trend estimation atmultiple samples of the series of samples of the at least one sensorsignal based at least partly on a trend estimation at a previous sampleand a growth term.

In one or more example embodiments, an apparatus may include: acontroller to obtain a series of samples of at least one sensor signalthat is responsive to a blood glucose level of a patient, and thecontroller may include one or more processors to: determine, based atleast partly on the series of samples, at least one metric assessing anunderlying trend of a change in responsiveness of the at least onesensor signal to the blood glucose level of the patient over time; andassess a reliability of the at least one sensor signal to respond to theblood glucose level of the patient based at least partly on the at leastone metric assessing an underlying trend.

In at least one example implementation, the one or more processors ofthe controller may further be to: generate an alert signal responsive toa comparison of the at least one metric assessing an underlying trendwith at least one predetermined threshold.

In at least one example implementation, the controller may be capable ofassessing by: comparing the at least one metric assessing an underlyingtrend with at least a first predetermined threshold and a secondpredetermined threshold. In at least one other example implementation,the controller may be further capable of assessing by: assessing thatthe reliability of the at least one sensor signal is in a first stateresponsive to a comparison of the at least one metric assessing anunderlying trend with the first predetermined threshold; assessing thatthe reliability of the at least one sensor signal is in a second stateresponsive to a comparison of the at least one metric assessing anunderlying trend with the first predetermined threshold and the secondpredetermined threshold; and assessing that the reliability of the atleast one sensor signal is in a third state responsive to a comparisonof the at least one metric assessing an underlying trend with the secondpredetermined threshold. In at least one other example implementation,the controller may be further capable of assessing by: ascertaining atleast one value indicating a severity of divergence by the at least onesensor signal from the blood glucose level of the patient over timebased at least partly on the at least one metric assessing an underlyingtrend, the first predetermined threshold, and the second predeterminedthreshold.

In at least one example implementation, the one or more processors ofthe controller may further be to: acquire the at least one sensor signalfrom one or more subcutaneous glucose sensors, wherein the at least onemetric assessing an underlying trend may reflect an apparent reliabilityof the at least one sensor signal that is acquired from the one or moresubcutaneous glucose sensors. In at least one example implementation,the one or more processors of the controller may further be to: alter aninsulin infusion treatment for the patient responsive at least partly tothe assessed reliability of the at least one sensor signal.

In at least one example implementation, the controller may be capable ofdetermining by: producing the at least one metric assessing anunderlying trend using a slope of a linear regression that is derived atleast partly from the series of samples of the at least one sensorsignal. In at least one example implementation, the one or moreprocessors of the controller may further be to: transform the series ofsamples of the at least one sensor signal to derive a monotonic curve,wherein the controller may be capable of producing the at least onemetric assessing an underlying trend by calculating the slope of thelinear regression, with the linear regression being derived at leastpartly from the monotonic curve.

In at least one example implementation, the controller may be capable ofdetermining by: decomposing the at least one sensor signal asrepresented by the series of samples using at least one empirical modedecomposition and one or more spline functions to remove relativelyhigher frequency components from the at least one sensor signal. In atleast one example implementation, the controller may be capable ofdetermining by: decomposing the at least one sensor signal asrepresented by the series of samples using at least one discrete wavelettransform; and reconstructing a smoothed signal from one or moreapproximation coefficients resulting from the at least one discretewavelet transform. In at least one example implementation, thecontroller may be capable of determining by: iteratively updating atrend estimation at multiple samples of the series of samples of the atleast one sensor signal based at least partly on a trend estimation at aprevious sample and a growth term.

In one or more example embodiments, a system may include: means forobtaining a series of samples of at least one sensor signal that isresponsive to a blood glucose level of a patient; means for determining,based at least partly on the series of samples, at least one metricassessing an underlying trend of a change in responsiveness of the atleast one sensor signal to the blood glucose level of the patient overtime; and means for assessing a reliability of the at least one sensorsignal to respond to the blood glucose level of the patient based atleast partly on the at least one metric assessing an underlying trend.

In one or more example embodiments, an article may include at least onestorage medium having stored thereon instructions executable by one ormore processors to: obtain a series of samples of at least one sensorsignal that is responsive to a blood glucose level of a patient;determine, based at least partly on the series of samples, at least onemetric assessing an underlying trend of a change in responsiveness ofthe at least one sensor signal to the blood glucose level of the patientover time; and assess a reliability of the at least one sensor signal torespond to the blood glucose level of the patient based at least partlyon the at least one metric assessing an underlying trend.

Other alternative example embodiments are described herein and/orillustrated in the accompanying Drawings. Additionally, particularexample embodiments may be directed to an article comprising a storagemedium including machine-readable instructions stored thereon which, ifexecuted by a special purpose computing device and/or processor, may bedirected to enable the special purpose computing device/processor toexecute at least a portion of described method(s) according to one ormore particular implementations. In other particular exampleembodiments, a sensor may be adapted to generate one or more signalsresponsive to a measured blood glucose concentration in a body while aspecial purpose computing device and/or processor may be adapted toperform at least a portion of described method(s) according to one ormore particular implementations based upon the one or more signalsgenerated by the sensor.

BRIEF DESCRIPTION OF THE FIGURES

Non-limiting and non-exhaustive features are described with reference tothe following figures, wherein like reference numerals refer to likeand/or analogous parts throughout the various figures:

FIG. 1 is a schematic diagram of an example closed loop glucose controlsystem in accordance with an embodiment.

FIG. 2 is a front view of example closed loop hardware located on a bodyin accordance with an embodiment.

FIG. 3( a) is a perspective view of an example glucose sensor system foruse in accordance with an embodiment.

FIG. 3( b) is a side cross-sectional view of a glucose sensor system ofFIG. 3( a) for an embodiment.

FIG. 3( c) is a perspective view of an example sensor set for a glucosesensor system of FIG. 3( a) for use in accordance with an embodiment.

FIG. 3( d) is a side cross-sectional view of a sensor set of FIG. 3( c)for an embodiment.

FIG. 4 is a cross sectional view of an example sensing end of a sensorset of FIG. 3( d) for use in accordance with an embodiment.

FIG. 5 is a top view of an example infusion device with a reservoir doorin an open position, for use according to an embodiment.

FIG. 6 is a side view of an example infusion set with an insertionneedle pulled out, for use according to an embodiment.

FIG. 7 is a cross-sectional view of an example sensor set and an exampleinfusion set attached to a body in accordance with an embodiment.

FIG. 8( a) is a diagram of an example single device and its componentsfor a glucose control system in accordance with an embodiment.

FIG. 8( b) is a diagram of two example devices and their components fora glucose control system in accordance with an embodiment.

FIG. 8( c) is another diagram of two example devices and theircomponents for a glucose control system in accordance with anembodiment.

FIG. 8( d) is a diagram of three example devices and their componentsfor a glucose control system in accordance with an embodiment.

FIG. 9 is a schematic diagram of an example closed loop system tocontrol blood glucose levels via insulin infusion and/or glucagoninfusion using at least a controller based on glucose level feedback viaa sensor signal in accordance with an embodiment.

FIG. 10 is a schematic diagram of at least a portion of an examplecontroller including a sensor signal reliability analyzer that mayinclude a non-physiological anomaly detector and/or a responsivenessdetector in accordance with an embodiment.

FIG. 11 is a schematic diagram of an example non-physiological anomalydetector that may include a sensor signal purity analyzer in accordancewith an embodiment.

FIG. 12 is a flow diagram of an example method for handlingnon-physiological anomalies that may be present in a glucose sensorsignal in accordance with an embodiment.

FIGS. 13A and 13B depict graphical diagrams that illustrate examplecomparisons between sensor signal values and measured blood glucosevalues in relation to non-physiological anomalies for first and secondsensors, respectively, in accordance with an embodiment.

FIG. 14 is a schematic diagram of an example responsiveness detectorthat may include a sensor signal stability analyzer in accordance withan embodiment.

FIG. 15 is a flow diagram of an example method for handling apparentchanges in responsiveness of a glucose sensor signal to blood glucoselevels of a patient in accordance with an embodiment.

FIG. 16A depicts a graphical diagram that illustrates an example of adownward drifting sensor signal along with physiological activity inaccordance with an embodiment.

FIGS. 16B and 16C depict graphical diagrams that illustrate multipleexample glucose signals and corresponding monotonic fundamental signaltrends as generated by first and second example signal trend analysisapproaches, respectively, in accordance with an embodiment.

FIG. 17 is a schematic diagram of an example controller that producesoutput information based on input data in accordance with an embodiment.

DETAILED DESCRIPTION

In an example glucose monitoring sensor and/or insulin delivery systemenvironment, measurements reflecting blood-glucose levels may beemployed in a closed loop infusion system for regulating a rate of fluidinfusion into a body. In particular example embodiments, a sensor and/orsystem may be adapted to regulate a rate of insulin and/or glucagoninfusion into a body of a patient based, at least in part, on a glucoseconcentration measurement taken from a body (e.g., from a blood-glucosesensor, including a current sensor). In certain example implementations,such a system may be designed to model a pancreatic beta cell (β-cell).Here, such a system may control an infusion device to release insulininto a body of a patient in an at least approximately similarconcentration profile as might be created by fully functioning humanβ-cells, if such were responding to changes in blood glucoseconcentrations in the body. Thus, such a closed loop infusion system maysimulate a body's natural insulin response to blood glucose levels.Moreover, it may not only make efficient use of insulin, but it may alsoaccount for other bodily functions as well because insulin can have bothmetabolic and mitogenic effects.

According to certain embodiments, examples of closed-loop systems asdescribed herein may be implemented in a hospital environment to monitorand/or control levels of glucose and/or insulin in a patient. Here, aspart of a hospital or other medical facility procedure, a caretaker orattendant may be tasked with interacting with a closed-loop system to,for example: enter blood-glucose reference measurement samples intocontrol equipment to calibrate blood glucose measurements obtained fromblood-glucose sensors, make manual adjustments to devices, and/or makechanges to therapies, just to name a few examples. Alternatively,according to certain embodiments, examples of closed-loop systems asdescribed herein may be implemented in non-hospital environments tomonitor and/or control levels of glucose and/or insulin in a patient.Here, a patient or other non-medical professional may be involved ininteracting with a closed-loop system.

However, while a closed-loop glucose control system is active, oversightby medical professionals, patients, non-medical professionals, etc. istypically reduced. Such a closed-loop glucose control system may becomeat least partially responsible for the health, and possibly thesurvival, of a diabetic patient. To more accurately control bloodglucose levels of a patient, a closed-loop system may be providedknowledge of a current blood glucose level. One approach to providingsuch knowledge is implementation of a blood glucose sensor, such asincluding one or more such glucose sensors in a closed-loop system.

A closed-loop system may receive at least one glucose sensor signal fromone or more glucose sensors, with the glucose sensor signal intended toaccurately represent a current (or at least relatively current) bloodglucose level. If a glucose sensor signal indicates that a blood glucoselevel is currently too high, then a closed-loop system may takeaction(s) to lower it. On the other hand, if a glucose sensor signalindicates that a blood glucose level is currently too low, then aclosed-loop system may take action(s) to raise it. Actions taken by aclosed-loop system to control blood glucose levels of a patient andprotect the patient's health may therefore be based at least partly on aglucose sensor signal received from a glucose sensor.

Unfortunately, a received glucose sensor signal may not be completelyreliable as a representation of a current blood glucose level of apatient. For example, a received signal may include impurities thatobscure a blood glucose level that actually exists in a body currently.By way of example but not limitation, impurities may be introduced if asensor measures an incorrect blood glucose level (e.g., due to localizedpressure at a sensor site, due to improper sensor hydration, due toinflammatory response, etc.), if noise or other factors impact a bloodglucose level signal after measurement, combinations thereof, and soforth. Alternatively and/or additionally, a glucose sensor may graduallybecome increasingly less stable in its responsiveness, such as bybecoming increasingly less capable of accurately measuring a currentblood glucose level. In such situations (and/or other ones), a glucosesensor signal that is received at a controller of a closed-loop systemmay not be sufficiently reliable to justify entrusting a patient's lifeand health to its control decisions.

In certain embodiments that are described herein, a closed loop systemmay assess a reliability of at least one sensor signal with respect toits ability to accurately reflect a blood glucose level of a patientbased at least partly on at least one metric. In an example embodiment,a metric may characterize one or more non-physiological anomalies of arepresentation of a blood glucose level of a patient by at least onesensor signal. In another example embodiment, a metric may assess anunderlying trend of a change in responsiveness of at least one sensorsignal to a blood glucose level of a patient over time. These and otherexample implementations are described further herein below.

FIG. 1 is a block diagram of an example closed loop glucose controlsystem 5 in accordance with an embodiment. Particular embodiments mayinclude a glucose sensor system 10, a controller 12, an insulin deliverysystem 14, and a glucagon delivery system 15, etc. as shown in FIG. 1.In certain example embodiments, glucose sensor system 10 may generate asensor signal 16 representative of blood glucose levels 18 in body 20,and glucose sensor system 10 may provide sensor signal 16 to controller12. Controller 12 may receive sensor signal 16 and generate commands 22that are communicated at least to insulin delivery system 14 and/orglucagon delivery system 15. Insulin delivery system 14 may receivecommands 22 and infuse insulin 24 into body 20 in response to commands22. Likewise, glucagon delivery system 15 may receive commands 22 fromcontroller 12 and infuse glucagon 25 into body 20 in response tocommands 22.

Glucose sensor system 10 may include, by way of example but notlimitation, a glucose sensor; sensor electrical components to providepower to a glucose sensor and to generate sensor signal 16; a sensorcommunication system to carry sensor signal 16 to controller 12; asensor system housing for holding, covering, and/or containingelectrical components and a sensor communication system; any combinationthereof, and so forth.

Controller 12 may include, by way of example but not limitation,electrical components, other hardware, firmware, and/or software, etc.to generate commands 22 for insulin delivery system 14 and/or glucagondelivery system 15 based at least partly on sensor signal 16. Controller12 may also include a controller communication system to receive sensorsignal 16 and/or to provide commands 22 to insulin delivery system 14and/or glucagon delivery system 15. In particular exampleimplementations, controller 12 may include a user interface and/oroperator interface (e.g., a human interface as shown in FIG. 9)comprising a data input device and/or a data output device. Such a dataoutput device may, for example, generate signals to initiate an alarmand/or include a display or printer for showing a status of controller12 and/or a patient's vital indicators, monitored historical data,combinations thereof, and so forth. Such a data input device maycomprise dials, buttons, pointing devices, manual switches, alphanumerickeys, a touch-sensitive display, combinations thereof, and/or the likefor receiving user and/or operator inputs. It should be understood,however, that these are merely examples of input and output devices thatmay be a part of an operator and/or user interface and that claimedsubject matter is not limited in these respects.

Insulin delivery system 14 may include an infusion device and/or aninfusion tube to infuse insulin 24 into body 20. Similarly, glucagondelivery system 15 may include an infusion device and/or an infusiontube to infuse glucagon 25 into body 20. In alternative embodiments,insulin 24 and glucagon 25 may be infused into body 20 using a sharedinfusion tube. In other alternative embodiments, insulin 24 and/orglucagon 25 may be infused using an intravenous system for providingfluids to a patient (e.g., in a hospital or other medical environment).When an intravenous system is employed, glucose may be infused directlyinto a bloodstream of a body instead of or in addition to infusingglucagon into interstitial tissue. It should also be understood thatcertain example embodiments for closed loop glucose control system 5 mayinclude an insulin delivery system 14 without a glucagon delivery system15 (or vice versa).

In particular example embodiments, an infusion device (not explicitlyidentified in FIG. 1) may include infusion electrical components toactivate an infusion motor according to commands 22; an infusioncommunication system to receive commands 22 from controller 12; aninfusion device housing (not shown) to hold, cover, and/or contain theinfusion device; any combination thereof; and so forth.

In particular example embodiments, controller 12 may be housed in aninfusion device housing, and an infusion communication system maycomprise an electrical trace or a wire that carries commands 22 fromcontroller 12 to an infusion device. In alternative embodiments,controller 12 may be housed in a sensor system housing, and a sensorcommunication system may comprise an electrical trace or a wire thatcarries sensor signal 16 from sensor electrical components to controllerelectrical components. In other alternative embodiments, controller 12may have its own housing or may be included in a supplemental device. Inyet other alternative embodiments, controller 12 may be co-located withan infusion device and a sensor system within one shared housing. Infurther alternative embodiments, a sensor, a controller, and/or infusioncommunication systems may utilize a cable; a wire; a fiber optic line;RF, IR, or ultrasonic transmitters and receivers; combinations thereof;and/or the like instead of electrical traces, just to name a fewexamples.

Overview of Example Systems

FIGS. 2-6 illustrate example glucose control systems in accordance withcertain embodiments. FIG. 2 is a front view of example closed loophardware located on a body in accordance with certain embodiments. FIGS.3( a)-3(d) and 4 show different views and portions of an example glucosesensor system for use in accordance with certain embodiments. FIG. 5 isa top view of an example infusion device with a reservoir door in anopen position in accordance with certain embodiments. FIG. 6 is a sideview of an example infusion set with an insertion needle pulled out inaccordance with certain embodiments.

Particular example embodiments may include a sensor 26, a sensor set 28,a telemetered characteristic monitor 30, a sensor cable 32, an infusiondevice 34, an infusion tube 36, and an infusion set 38, any or all ofwhich may be worn on a body 20 of a user or patient, as shown in FIG. 2.As shown in FIGS. 3( a) and 3(b), telemetered characteristic monitor 30may include a monitor housing 31 that supports a printed circuit board33, battery or batteries 35, antenna (not shown), a sensor cableconnector (not shown), and so forth. A sensing end 40 of sensor 26 mayhave exposed electrodes 42 that may be inserted through skin 46 into asubcutaneous tissue 44 of a user's body 20, as shown in FIGS. 3( d) and4. Electrodes 42 may be in contact with interstitial fluid (ISF) that isusually present throughout subcutaneous tissue 44.

Sensor 26 may be held in place by sensor set 28, which may be adhesivelysecured to a user's skin 46, as shown in FIGS. 3( c) and 3(d). Sensorset 28 may provide for a connector end 27 of sensor 26 to connect to afirst end 29 of sensor cable 32. A second end 37 of sensor cable 32 mayconnect to monitor housing 31. Batteries 35 that may be included inmonitor housing 31 provide power for sensor 26 and electrical components39 on printed circuit board 33. Electrical components 39 may samplesensor signal 16 (e.g., of FIG. 1) and store digital sensor values(Dsig) in a memory. Digital sensor values Dsig may be periodicallytransmitted from a memory to controller 12, which may be included in aninfusion device.

With reference to FIGS. 2 and 5 (and FIG. 1), a controller 12 mayprocess digital sensor values Dsig and generate commands 22 (e.g., ofFIG. 1) for infusion device 34. Infusion device 34 may respond tocommands 22 and actuate a plunger 48 that forces insulin 24 (e.g., ofFIG. 1) out of a reservoir 50 that is located inside an infusion device34. Glucose may be infused from a reservoir responsive to commands 22using a similar and/or analogous device (not shown). In alternativeimplementations, glucose may be administered to a patient orally.

In particular example embodiments, a connector tip 54 of reservoir 50may extend through infusion device housing 52, and a first end 51 ofinfusion tube 36 may be attached to connector tip 54. A second end 53 ofinfusion tube 36 may connect to infusion set 38 (e.g., of FIGS. 2 and6). With reference to FIG. 6 (and FIG. 1), insulin 24 (e.g., of FIG. 1)may be forced through infusion tube 36 into infusion set 38 and intobody 16 (e.g., of FIG. 1). Infusion set 38 may be adhesively attached toa user's skin 46. As part of infusion set 38, a cannula 56 may extendthrough skin 46 and terminate in subcutaneous tissue 44 to completefluid communication between a reservoir 50 (e.g., of FIG. 5) andsubcutaneous tissue 44 of a user's body 16.

In example alternative embodiments, as pointed out above, a closed-loopsystem in particular implementations may be a part of a hospital-basedglucose management system. Given that insulin therapy during intensivecare has been shown to dramatically improve wound healing and reduceblood stream infections, renal failure, and polyneuropathy mortality,irrespective of whether subjects previously had diabetes (See, e.g., Vanden Berghe G. et al. NEJM 345: 1359-67, 2001), particular exampleimplementations may be used in a hospital setting to control a bloodglucose level of a patient in intensive care. In such alternativeembodiments, because an intravenous (IV) hookup may be implanted into apatient's arm while the patient is in an intensive care setting (e.g.,ICU), a closed loop glucose control may be established that piggy-backsoff an existing IV connection. Thus, in a hospital or othermedical-facility based system, IV catheters that are directly connectedto a patient's vascular system for purposes of quickly delivering IVfluids, may also be used to facilitate blood sampling and directinfusion of substances (e.g., insulin, glucose, anticoagulants, etc.)into an intra-vascular space.

Moreover, glucose sensors may be inserted through an IV line to provide,e.g., real-time glucose levels from the blood stream. Therefore,depending on a type of hospital or other medical-facility based system,such alternative embodiments may not necessarily utilize all of thedescribed system components. Examples of components that may be omittedinclude, but are not limited to, sensor 26, sensor set 28, telemeteredcharacteristic monitor 30, sensor cable 32, infusion tube 36, infusionset 38, and so forth. Instead, standard blood glucose meters and/orvascular glucose sensors, such as those described in co-pending U.S.Patent Application Publication No. 2008/0221509 (U.S. patent applicationSer. No. 12/121,647; to Gottlieb, Rebecca et al.; entitled “MULTILUMENCATHETER”), filed 15 May 2008, may be used to provide blood glucosevalues to an infusion pump control, and an existing IV connection may beused to administer insulin to an patient. Other alternative embodimentsmay also include fewer, more, and/or different components than thosethat are described herein and/or illustrated in the accompanyingDrawings.

Example System and/or Environmental Delays

Example system and/or environmental delays are described herein.Ideally, a sensor and associated component(s) would be capable ofproviding a real time, noise-free measurement of a parameter, such as ablood glucose measurement, that a control system is intended to control.However, in real-world implementations, there are typicallyphysiological, chemical, electrical, algorithmic, and/or other sourcesof time delays that cause a sensor measurement to lag behind an actualpresent value. Also, as noted herein, such a delay may arise from, forinstance, a particular level of noise filtering that is applied to asensor signal.

FIG. 7 is a cross-sectional view of an example sensor set and an exampleinfusion set that is attached to a body in accordance with anembodiment. In particular example implementations, as shown in FIG. 7, aphysiological delay may arise from a time that transpires while glucosemoves between blood plasma 420 and interstitial fluid (ISF). Thisexample delay may be represented by a circled double-headed arrow 422.As discussed above with reference to FIG. 2-6, a sensor may be insertedinto subcutaneous tissue 44 of body 20 such that electrode(s) 42 (e.g.,of FIGS. 3 and 4) near a tip, or sending end 40, of sensor 26 are incontact with ISF. However, a parameter to be measured may include aconcentration of glucose in blood.

Glucose may be carried throughout a body in blood plasma 420. Through aprocess of diffusion, glucose may move from blood plasma 420 into ISF ofsubcutaneous tissue 44 and vice versa. As blood glucose level 18 (e.g.,of FIG. 1) changes, so does a glucose level of ISF. However, a glucoselevel of ISF may lag behind blood glucose level 18 due to a timerequired for a body to achieve glucose concentration equilibrium betweenblood plasma 420 and ISF. Some studies have shown that glucose lag timesbetween blood plasma and ISF may vary between, e.g., 0 to 30 minutes.Some parameters that may affect such a glucose lag time between bloodplasma and ISF are an individual's metabolism, a current blood glucoselevel, whether a glucose level is rising or falling, combinationsthereof, and so forth, just to name a few examples.

A chemical reaction delay 424 may be introduced by sensor responsetimes, as represented by a circle 424 that surrounds a tip of sensor 26in FIG. 7. Sensor electrodes 42 (e.g., of FIGS. 3 and 4) may be coatedwith protective membranes that keep electrodes 42 wetted with ISF,attenuate the glucose concentration, and reduce glucose concentrationfluctuations on an electrode surface. As glucose levels change, suchprotective membranes may slow the rate of glucose exchange between ISFand an electrode surface. In addition, there may be chemical reactiondelay(s) due to a reaction time for glucose to react with glucoseoxidase GOX to generate hydrogen peroxide and a reaction time for asecondary reaction, such as a reduction of hydrogen peroxide to water,oxygen, and free electrons.

Thus, an insulin delivery delay may be caused by a diffusion delay,which may be a time for insulin that has been infused into a tissue todiffuse into the blood stream. Other contributors to insulin deliverydelay may include, but are not limited to: a time for a delivery systemto deliver insulin to a body after receiving a command to infuseinsulin; a time for insulin to spread throughout a circulatory systemonce it has entered the blood stream; and/or by other mechanical,electrical/electronic, or physiological causes alone or in combination,just to name a few examples. In addition, a body clears insulin evenwhile an insulin dose is being delivered from an insulin delivery systeminto the body. Because insulin is continuously cleared from blood plasmaby a body, an insulin dose that is delivered to blood plasma too slowlyor is delayed is at least partially, and possibly significantly, clearedbefore the entire insulin dose fully reaches blood plasma. Therefore, aninsulin concentration profile in blood plasma may never achieve a givenpeak (nor follow a given profile) that it may have achieved if therewere no delay.

Moreover, there may also be a processing delay as an analog sensorsignal Isig is converted to digital sensor values Dsig. In particularexample embodiments, an analog sensor signal Isig may be integrated overone-minute intervals and converted to a number of counts. Thus, in sucha case, an analog-to-digital (A/D) conversion time may result in anaverage delay of 30 seconds. In particular example embodiments,one-minute values may be averaged into 5-minute values before they areprovided to controller 12 (e.g., of FIG. 1). A resulting average delaymay be two-and-one-half minutes (e.g., half of the averaging interval).In example alternative embodiments, longer or shorter integration timesmay be used that result in longer or shorter delay times.

In other example embodiments, an analog sensor signal current Isig maybe continuously converted to an analog voltage Vsig, and an A/Dconverter may sample voltage Vsig every 10 seconds. Thus, in such acase, six 10-second values may be pre-filtered and averaged to create aone-minute value. Also, five one-minute values may be filtered andaveraged to create a five-minute value that results in an average delayof two-and-one-half minutes. In other alternative embodiments, othersensor signals from other types of sensors may be converted to digitalsensor values Dsig as appropriate before transmitting the digital sensorvalues Dsig to another device. Moreover, other embodiments may use otherelectrical components, other sampling rates, other conversions, otherdelay periods, a combination thereof, and so forth.

System Configuration Examples

FIG. 8( a)-8(d) illustrate example diagrams of one or more devices andtheir components for glucose control systems in accordance with certainembodiments. These FIG. 8( a)-8(d) show exemplary, but not limiting,illustrations of components that may be utilized with certaincontroller(s) that are described herein above. Various changes incomponents, layouts of such components, combinations of elements, and soforth may be made without departing from the scope of claimed subjectmatter.

Before it is provided as an input to controller 12 (e.g., of FIG. 1), asensor signal 16 may be subjected to signal conditioning such aspre-filtering, filtering, calibrating, and so forth, just to name a fewexamples. Components such as a pre-filter, one or more filters, acalibrator, controller 12, etc. may be separately partitioned orphysically located together (e.g., as shown in FIG. 8( a)), and they maybe included with a telemetered characteristic monitor transmitter 30, aninfusion device 34, a supplemental device, and so forth.

In particular example embodiments, a pre-filter, filter(s), and acalibrator may be included as part of telemetered characteristic monitortransmitter 30, and a controller (e.g., controller 12) may be includedwith infusion device 34, as shown in FIG. 8( b). In example alternativeembodiments, a pre-filter may be included with telemeteredcharacteristic monitor transmitter 30, and a filter and calibrator maybe included with a controller in an infusion device, as shown in FIG. 8(c). In other alternative example embodiments, a pre-filter may beincluded with telemetered characteristic monitor transmitter 30, whilefilter(s) and a calibrator are included in supplemental device 41, and acontroller may be included in the infusion device, as shown in FIG. 8(d).

In particular example embodiments, a sensor system may generate amessage that includes information based on a sensor signal such asdigital sensor values, pre-filtered digital sensor values, filtereddigital sensor values, calibrated digital sensor values, commands, andso forth, just to name a few examples. Such a message may include othertypes of information as well, including, by way of example but notlimitation, a serial number, an ID code, a check value, values for othersensed parameters, diagnostic signals, other signals, and so forth. Inparticular example embodiments, digital sensor values Dsig may befiltered in a telemetered characteristic monitor transmitter 30, andfiltered digital sensor values may be included in a message sent toinfusion device 34 where the filtered digital sensor values may becalibrated and used in a controller. In other example embodiments,digital sensor values Dsig may be filtered and calibrated beforetransmission to a controller in infusion device 34. Alternatively,digital sensor values Dsig may be filtered, calibrated, and used in acontroller to generate commands 22 that are sent from telemeteredcharacteristic monitor transmitter 30 to infusion device 34.

In further example embodiments, additional components, such as apost-calibration filter, a display, a recorder, a blood glucose meter,etc. may be included in devices with any of the other components, orthey may stand-alone. If a blood glucose meter is built into a device,for instance, it may be co-located in the same device that contains acalibrator. In alternative example embodiments, more, fewer, and/ordifferent components may be implemented than those that are shown inFIG. 8 and/or described herein above.

In particular example embodiments, RF telemetry may be used tocommunicate between devices that contain one or more components, such astelemetered characteristic monitor transmitter 30 and infusion device34. In alternative example embodiments, other communication mediums maybe employed between devices, such as wireless wide area network (WAN)(e.g., cell communication), Wi-Fi, wires, cables, IR signals, lasersignals, fiber optics, ultrasonic signals, and so forth, just to name afew examples.

Example Approaches to Glucose Sensor Signal Reliability Analysis

FIG. 9 is a schematic diagram of an example closed loop system 900 tocontrol blood glucose levels via insulin infusion and/or glucagoninfusion using at least a controller based on glucose level feedback viaa sensor signal in accordance with an embodiment. In particular exampleembodiments, a closed loop control system may be used for deliveringinsulin to a body to compensate for β-cells that perform inadequately.There may be a desired basal blood glucose level G_(B) for a particularbody. A difference between a desired basal blood glucose level G_(B) andan estimate of a present blood glucose level G is the glucose levelerror G_(E) that may be corrected. For particular example embodiments,glucose level error G_(E) may be provided as an input to controller 12,as shown in FIG. 9. Although at least a portion of controller 12 may berealized as a proportional-integral-derivative (PID) controller, claimedsubject matter is not so limited, and controller 12 may be realized inalternative manners.

If glucose level error G_(E) is positive (meaning, e.g., that a presentestimate of blood glucose level G is higher than a desired basal bloodglucose level G_(B)), then a command from controller 12 may generate acommand 22 to drive insulin delivery system 34 to provide insulin 24 tobody 20. Insulin delivery system 34 may be an example implementation ofinsulin delivery system 14 (e.g., of FIG. 1). Likewise, if G_(E) isnegative (meaning, e.g., that a present estimate of blood glucose levelG is lower than a desired basal blood glucose level G_(B)), then acommand from controller 12 may generate a command 22 to drive glucagondelivery system 35 to provide glucagon 25 to body 20. Glucagon deliverysystem 35 may be an example implementation of glucagon delivery system15 (e.g., of FIG. 1).

Closed loop system 900 may also include and/or be in communication witha human interface 65. Example implementations for a human interface 65are described herein above with particular reference to FIG. 1 in thecontext of an output device. As shown, human interface 65 may receiveone or more commands 22 from controller 12. Such commands 22 mayinclude, by way of example but not limitation, one or more commands tocommunicate information to a user (e.g., a patient, a healthcareprovider, etc.) visually, audibly, haptically, some combination thereof,and so forth. Such information may include data, an alert, or some othernotification 55. Human interface 65 may include a screen, a speaker, avibration mechanism, any combination thereof, and so forth, just to namea few examples. Hence, in response to receiving a command 22 fromcontroller 12, human interface 65 may present at least one notification55 to a user via a screen, a speaker, a vibration, and so forth.

In terms of a control loop for purposes of discussion, glucose may beconsidered to be positive, and therefore insulin may be considered to benegative. Sensor 26 may sense an ISF glucose level of body 20 andgenerate a sensor signal 16. For certain example embodiments, a controlloop may include a filter and/or calibration unit 456 and/or correctionalgorithm(s) 454. However, this is by way of example only, and claimedsubject matter is not so limited. Sensor signal 16 may be filtered/orand calibrated at unit 456 to create an estimate of present bloodglucose level 452. Although shown separately, filter and/or calibrationunit 456 may be integrated with controller 12 without departing fromclaimed subject matter. Moreover, filter and/or calibration unit 456 mayalternatively be realized as part of controller 12 (or vice versa)without departing from claimed subject matter.

In particular example embodiments, an estimate of present blood glucoselevel G may be adjusted with correction algorithms 454 before it iscompared with a desired basal blood glucose level G_(B) to calculate anew glucose level error G_(E) to start a loop again. Also, an attendant,a caretaker, a patient, etc. may obtain blood glucose reference samplemeasurements from a patient's blood using, e.g., glucose test strips.These blood-based sample measurements may be used to calibrate ISF-basedsensor measurements, e.g. using techniques such as those described inU.S. Pat. No. 6,895,263, issued 17 May 2005, and/or other techniques.Although shown separately, a correction algorithms unit 454 may beintegrated with controller 12 without departing from claimed subjectmatter. Moreover, correction algorithms unit 454 may alternatively berealized as part of controller 12 (or vice versa) without departing fromclaimed subject matter. Similarly, a difference unit and/or otherfunctionality for calculating G_(E) from G and G_(B) may be incorporatedas part of controller 12 without departing from claimed subject matter.

For an example PID-type of controller 12, if a glucose level error G_(E)is negative (meaning, e.g., that a present estimate of blood glucoselevel is lower than a desired basal blood glucose level G_(B)), thencontroller 12 may reduce or stop insulin delivery depending on whetheran integral component response of a glucose error G_(E) is stillpositive. In alternative embodiments, as discussed below, controller 12may initiate infusion of glucagon 25 if glucose level error G_(E) isnegative. If a glucose level error G_(E) is zero (meaning, e.g., that apresent estimate of blood glucose level is equal to a desired basalblood glucose level G_(B)), then controller 12 may or may not issuecommands to infuse insulin 24 or glucagon 25, depending on a derivativecomponent (e.g., whether a glucose level is rising or falling) and/or anintegral component (e.g., how long and by how much a glucose level hasbeen above or below basal blood glucose level G_(B)).

To more clearly understand the effects that a body has on such a controlloop, a more detailed description of example physiological effects thatinsulin may have on glucose concentration in ISF is provided. Inparticular example embodiments, infusion delivery system 34 may deliverinsulin into ISF of subcutaneous tissue 44 (e.g., also of FIGS. 3, 4,and 6) of body 20. Alternatively, insulin delivery system 34 or aseparate infusion device (e.g., glucagon delivery system 35) maysimilarly deliver glucose and/or glucagon into ISF of subcutaneoustissue 44. Here, insulin 24 may diffuse from local ISF surrounding acannula into blood plasma and spread throughout body 20 in a maincirculatory system (e.g., as represented by blood stream 47). Infusedinsulin may diffuse from blood plasma into ISF substantially throughoutthe entire body.

Here in the body, insulin 24 may bind with and activate membranereceptor proteins on cells of body tissues. This may facilitate glucosepermeation into activated cells. In this way, tissues of body 20 maytake up glucose from ISF. As ISF glucose level decreases, glucose maydiffuse from blood plasma into ISF to maintain glucose concentrationequilibrium. Glucose in ISF may permeate a sensor membrane of sensor 26and affect sensor signal 16.

In addition, insulin may have direct and indirect effects on liverglucose production. Typically, increased insulin concentration maydecrease liver glucose production. Therefore, acute and immediateinsulin response may not only help a body to efficiently take upglucose, but it may also substantially stop a liver from adding toglucose in the blood stream. In alternative example embodiments, aspointed out above, insulin and/or glucose may be delivered more directlyinto the blood stream instead of into ISF, such as by delivery intoveins, arteries, the peritoneal cavity, and so forth, just to name a fewexamples. Accordingly, any time delay associated with moving insulinand/or glucose from ISF into blood plasma may be diminished. In otheralternative example embodiments, a glucose sensor may be in contact withblood or other body fluids instead of ISF, or a glucose sensor may beoutside of a body such that it may measure glucose through anon-invasive means. Embodiments using alternative glucose sensors mayhave shorter or longer delays between an actual blood glucose level anda measured blood glucose level.

A continuous glucose measuring sensor (CGMS) implementation for sensor26, for example, may detect a glucose concentration in ISF and provide aproportional current signal. A current signal (isig) may be linearlycorrelated with a reference blood glucose concentration (BG). Hence, alinear model, with two parameters (e.g., slope and offset), may be usedto calculate a sensor glucose concentration (SG) from sensor currentisig.

One or more controller gains may be selected so that commands from acontroller 12 direct infusion device 34 to release insulin 24 into body20 at a particular rate. Such a particular rate may cause insulinconcentration in blood to follow a similar concentration profile aswould be caused by fully functioning human β-cells responding to bloodglucose concentrations in a body. Similarly, controller gain(s) may beselected so that commands 22 from controller 12 direct an infusiondevice of glucagon delivery system 35 to release glucagon 25 in responseto insulin excursions. In particular example embodiments, controllergains may be selected at least partially by observing insulinresponse(s) of several normal glucose tolerant (NGT) individuals havinghealthy, normally-functioning β-cells.

In one or more example implementations, a system may additionallyinclude a communication unit 458. A communication unit 458 may comprise,by way of example but not limitation, a wireless wide area communicationmodule (e.g., a cell modem), a transmitter and/or a receiver (e.g., atransceiver), a Wi-Fi or Bluetooth chip or radio, some combinationthereof, and so forth. Communication unit 458 may receive signals from,by way of example but not limitation, filter and/or calibration unit456, sensor 26 (e.g., sensor signal 16), controller 12 (e.g. commands22), any combination thereof, and so forth. Although not specificallyshown in FIG. 9, communication unit 458 may also receive signals fromother units (e.g., correction algorithms unit 454, a delivery system 34and/or 35, human interface 65, etc.). Also, communication unit 458 maybe capable of providing signals to any of the other units of FIG. 9(e.g., controller 12, filter and/or calibration unit 456, humaninterface 65, etc.). Communication unit 458 may also be integrated withor otherwise form a part of another unit, such as controller 12 orfilter and/or calibration unit 456.

Communication unit 458 may be capable of transmitting calibrationoutput; calibration failure alarms; control algorithm states; sensorsignal alerts; and/or other physiological, hardware, and/or softwaredata (e.g., diagnostic data); and so forth to a remote data center foradditional processing and/or storage (e.g., for remote telemetrypurposes). These transmissions can be performed in response todiscovered/detected conditions, automatically, semi-automatically (e.g.,at the request of the remote data center), manually at the request ofthe patient, any combination thereof, and so forth, just to provide afew examples. The data can be subsequently served on request to remoteclients including, but not limited to, mobile phones, physician'sworkstations, patient's desktop computers, any combination of the above,and so forth, just to name a few examples. Communication unit 458 mayalso be capable of receiving from a remote location various information,including but not limited to: calibration information, instructions,operative parameters, other control information, some combinationthereof, and so forth. Such control information may be provided fromcommunication unit 458 to other system unit(s) (e.g., controller 12,filter and/or calibration unit 456, etc.).

FIG. 10 is a schematic diagram of at least a portion of an examplecontroller 12 including a sensor signal reliability analyzer 1002 thatmay include a non-physiological anomaly detector 1008 and/or aresponsiveness detector 1010 in accordance with an embodiment. Asillustrated, controller 12 may include a sensor signal reliabilityanalyzer 1002, and controller 12 may include or have access to a seriesof samples 1004 and may produce at least one alert signal 1006.

For certain example embodiments, series of samples 1004 may comprisemultiple samples taken from a sensor signal 16 (e.g., also of FIGS. 1and 9) at multiple sampling times. Thus, series of samples 1004 mayinclude multiple samples of at least one sensor signal, such as sensorsignal 16, and may be responsive to a blood glucose level of a patient.

Sensor signal reliability analyzer 1002 may consider one or more facetsof series of samples 1004 to assess at least one reliability aspect of asensor signal. Based at least partly on such assessment(s), sensorsignal reliability analyzer 1002 may produce at least one alert signal1006. Such an alert signal 1006 may be issued when an assessmentindicates that a sensor signal may not be sufficiently reliable so as tojustify entrusting a patient's health to closed-loop glucose controldecisions that are based on such an unreliable sensor signal. In exampleimplementations, an alert signal 1006 may comprise at least one command22 (e.g., also of FIGS. 1 and 9) that is issued from controller 12. Forinstance, an alert signal 1006 may be provided to a human interface 65(e.g., of FIG. 9) and/or an insulin delivery system 34 (e.g., of FIG.9). Alternatively and/or additionally, an alert signal 1006 may beprovided to another component and/or unit of (e.g., that is internal of)controller 12.

An example sensor signal reliability analyzer 1002 of a controller 12may include a non-physiological anomaly detector 1008 and/or aresponsiveness detector 1010. In certain example embodiments, anon-physiological anomaly detector 1008 may consider one or more facetsof series of samples 1004 to analyze at least one purity aspect of asensor signal. An alert signal 1006 may be issued if an assessmentindicates that a sensor signal may not be sufficiently pure inasmuch asit may additionally include artificial fluctuations that obscure a trueblood glucose level valuation. By way of example only, one or morenon-physiological anomalies may comprise artificial dynamics of at leastone sensor signal that do not correlate with or otherwise representblood glucose concentrations of a patient. In such situations,characterization of the one or more non-physiological anomalies maycomprise detection of the artificial dynamics of the at least one sensorsignal using the series of samples of the at least one sensor signal.Example embodiments for non-physiological anomaly detector 1008 aredescribed further herein below with particular reference to FIGS.11-13B.

In certain example embodiments, a responsiveness detector 1010 mayconsider one or more facets of series of samples 1004 to analyze atleast one stability aspect of a sensor signal. An alert signal 1006 maybe issued if an assessment indicates that a sensor signal may not besufficiently stable inasmuch as it may be drifting away from a trueblood glucose level valuation over time. By way of example only, anunderlying trend of series of samples 1004 may reflect a potentialdivergence by the at least one sensor signal from a blood glucose levelof a patient to an increasing extent over time due to a change inresponsiveness of the at least one sensor signal to the blood glucoselevel of the patient. Example embodiments for responsiveness detector1010 are described further herein below with particular reference toFIGS. 14-16C.

FIG. 11 is a schematic diagram of an example non-physiological anomalydetector 1008 that may include a sensor signal purity analyzer 1104 inaccordance with an embodiment. As illustrated, non-physiological anomalydetector 1008 may include or have access to a series of samples 1004, aquantitative deviation metric determiner 1102, a sensor signal purityanalyzer 1104, and an alert generator 1106. Quantitative deviationmetric determiner 1102 may estimate a quantitative deviation metric1108. Sensor signal purity analyzer 1104 may include at least one puritythreshold 1110.

For certain example embodiments, series of samples 1004 may be providedto quantitative deviation metric determiner 1102. Series of samples 1004may be obtained from at least one sensor signal (e.g., as shown in FIGS.9 and 10), and the at least one sensor signal may be acquired from oneor more subcutaneous glucose sensors (e.g., as shown in FIG. 9).Generally, a quantitative deviation metric determiner 1102 may determineat least one metric that quantitatively represents a deviation between ablood glucose level of a patient and at least one sensor signal.

More specifically, a quantitative deviation metric determiner 1102 maydetermine (e.g., calculate, estimate, ascertain, combinations thereof,etc.) at least one metric assessing a quantitative deviation (e.g.,quantitative deviation metric 1108) based at least in part on series ofsamples 1004 to characterize one or more non-physiological anomalies ofa representation of a blood glucose level of a patient by at least onesensor signal. In an example implementation, an at least one metricassessing a quantitative deviation may reflect an apparent reliabilityof at least one sensor signal that is generated by and acquired from oneor more subcutaneous glucose sensors. In another example implementation,an at least one metric assessing a quantitative deviation may reflect anoise level of at least one sensor signal and/or an artifact level ofthe at least one sensor signal. Quantitative deviation metric 1108 maybe provided to sensor signal purity analyzer 1104 (e.g., fromquantitative deviation metric determiner 1102).

In example embodiments, a quantitative deviation metric 1108 may reflectwhether and/or an extent to which a sensor signal is affected bynon-physiological anomalies, such as noise, sensor artifacts, suddensignal dropouts, motion-related artifacts, lost transmissions,combinations thereof, and so forth, just to name a few examples. By wayof example but not limitation, a quantitative deviation metric 1108 maybe related to a variance or a derivative thereof. For example, a metricassessing a quantitative deviation may comprise a representation of avariance of a random factor in a signal and/or samples thereof. Asanother example, a metric assessing a quantitative deviation maycomprise a representation of a variance expressed in a residual subspaceproduced by principal component analysis. However, these are merelyexamples of a metric assessing a quantitative deviation, and claimedsubject matter is not limited in these respects.

A sensor signal purity analyzer 1104 may perform at least one purityassessment with respect to at least one sensor signal based at least inpart on a metric assessing a quantitative deviation (e.g., quantitativedeviation metric 1108). Such a purity assessment may comprise at leastone comparison including a quantitative deviation metric 1108 and one ormore purity thresholds 1110 (e.g., at least one predeterminedthreshold). If a purity of a sensor signal is impaired because one ormore non-physiological anomalies are adversely affecting arepresentation of a blood glucose level of a patient by the sensorsignal, then sensor signal purity analyzer 1104 may cause alertgenerator 1106 to issue an alert signal 1006.

FIG. 12 is a flow diagram 1200 of an example method for handlingnon-physiological anomalies that may be present in a glucose sensorsignal in accordance with an embodiment. As illustrated, flow diagram1200 may include five operational blocks 1202-1210. Although operations1202-1210 are shown and described in a particular order, it should beunderstood that methods may be performed in alternative orders and/ormanners (including with a different number of operations) withoutdeparting from claimed subject matter. At least some operation(s) offlow diagram 1200 may be performed so as to be fully or partiallyoverlapping with other operation(s). Additionally, although thedescription below may reference particular aspects and featuresillustrated in certain other figures, methods may be performed withother aspects and/or features.

For certain example implementations, at operation 1202, a series ofsamples of at least one sensor signal that is responsive to a bloodglucose level of a patient may be obtained. At operation 1204, at leastone metric may be determined, based at least partly on the series ofsamples of the at least one sensor signal, to characterize one or morenon-physiological anomalies of a representation of the blood glucoselevel of the patient by the at least one sensor signal.

At operation 1206, a reliability of the at least one sensor signal torepresent the blood glucose level of the patient may be assessed basedat least partly on the at least one metric. At operation 1208, an alertsignal may be generated responsive to a comparison of the at least onemetric with at least one predetermined threshold. In an exampleimplementation, an alert may be generated by initiating a signal toindicate to a blood glucose controller that a sensor that generated theat least one sensor signal was not functioning reliably for at leastpart of a time while the series of samples was being obtained. Inanother example implementation, an alert may be generated by presentingat least one human-perceptible indication that a sensor that generatedthe at least one sensor signal was not functioning reliably for at leastpart of a time while the series of samples was being obtained.

At operation 1210, an insulin infusion treatment for the patient may bealtered responsive at least partly to the assessed reliability of the atleast one sensor signal. For example, an insulin infusion treatment fora patient may be altered by changing (e.g., increasing or decreasing) anamount of insulin being infused, by ceasing an infusion of insulin, bydelaying infusion until more samples are taken, by switching to adifferent sensor, by switching to a manual mode, by changing a relativeweighting applied to a given sensor or sensors and/or the samplesacquired there from, any combination thereof, and so forth, just to namea few examples.

For certain example implementations, a continuous glucose monitoringsensor may measure glucose concentration in ISF by oxidizing localizedglucose with the help of a glucose-oxidizing enzyme. Sensor output maybe a current signal (isig, nAmps) that is directly proportional toglucose concentration in ISF. Due to various reasons (e.g., immuneresponse, motion artifact, pressure on sensor-area, localized depletionof glucose, etc.), sensor current may display sudden artificial dynamicswhich do not necessarily correlate with dynamics of actual blood glucoselevels of a patient. Such artificial sensor dynamics may be classifiedas comprising or being related to sensor-noise and/orsensor-artifact(s).

One or more of various techniques may be implemented to detect suchsensor-noise and/or artifacts. By way of example but not limitation,fault detection by dynamic principal component analysis (DPCA) isdescribed below for detecting sensor-noise and/or sensor artifacts. PCAmay use multivariate statistics to reduce a number of dimensions ofsource data by projecting it onto a lower dimensional space. PCA mayinclude a linear transformation of original variables into a new set ofvariables that are uncorrelated to each other.

For an example implementation, let ‘x’ be a data vector. Here, ‘x’ maycontain a time series of samples of sensor current as shown in equation(1):x=[isig _(t),isig_(t-1), . . . ,isig_(t-n)]  (1)where,

-   -   t: current sampling point

The data vector ‘x’ may be centered by its mean and scaled by dividingwith its standard deviation as shown below in equation (2):

$\begin{matrix}{{\overset{\_}{x} = \frac{x - x_{AVG}}{x_{STD}}}{x_{STD} = \left\{ \begin{matrix}{x_{LB};} & {{{if}\mspace{14mu} x_{STD}} < x_{LB}} \\{x_{UB};} & {{{if}\mspace{14mu} x_{STD}} > x_{UB}} \\{x_{STD};} & {otherwise}\end{matrix} \right.}} & (2)\end{matrix}$where,

-   x_(AVG): mean of x-   x_(STD): standard deviation of x which is bounded by lowerbound    x_(LB) and upperbound x_(UB)

A dynamic matrix may be created by stacking the data vector ‘x’ in thefollowing manner:

$\begin{matrix}{Z = \begin{bmatrix}{\overset{\_}{x}}_{t} & {\overset{\_}{x}}_{t - 1} & \ldots & {\overset{\_}{x}}_{t - h} \\{\overset{\_}{x}}_{t - 1} & {\overset{\_}{x}}_{t - 2} & \ldots & {\overset{\_}{x}}_{t - h - 1} \\\vdots & \vdots & \ddots & \vdots \\{\overset{\_}{x}}_{t + h - n} & {\overset{\_}{x}}_{t + h - n - 1} & \ldots & {\overset{\_}{x}}_{t - n}\end{bmatrix}} & (3)\end{matrix}$A covariance of the Z-matrix (S) can be decomposed using singular valuedecomposition to obtain a matrix containing eigenvectors (P) (e.g., alsoknown as a loading matrix) and a diagonal matrix containing theeigenvalues Λ, as shown below:S=P·Λ·P ^(T)  (4)

Such transformed data may be written as shown in equation (5):y=P ^(T) ·z  (5)where,

-   -   z=[ x ₁, x _(t-1), . . . , x _(t-h)]^(T)

Original data can be represented by a smaller number of principalcomponents due to redundancy in data. This can result in one or moreeigenvalues being equal to (or close to) zero. Consequently, the first‘k’ (e.g., k may be equal to 2) eigenvalues, and their correspondingeigenvectors, may be used to form a PCA model, with other eigenvaluesand eigenvectors being omitted. New scaled principal components may bewritten as shown in equation (6):y=Λ _(k) ^(−1/2) ·P _(k) ^(T) ·z  (6)

Statistical quantities in a PCA model and a corresponding residual spacemay be checked by Hotelling's T² and/or Q statistics, respectively. T²statistics may indicate a quality of a model and may explain anormalized variance in a model subspace. Q statistics may indicate asize of a residual subspace and may represent a variance of randomnoise/artifacts expressed in the residual subspace.

Hotelling's T² statistic may be obtained by equation (7):T ² =y ^(T) ·y  (7)A Q statistic, which may be single-valued for each time point, for aresidual subspace may be determined using equation (8):Q=z ^(T)·(I−P _(k) ·P _(k) ^(T))·z  (8)When a Q statistic or statistics exceeds a predetermined (e.g., purity)threshold value (e.g., denoted Q_(TH)), one or more alerts may be issuedindicating random sensor-noise and/or artifacts are present to a degreethat indicates a sensor signal is unreliable.

In certain example implementations, determination of at least one metricmay therefore include ascertaining a residual portion of at least onesensor signal based at least in part on a series of samples of the atleast one sensor signal and determining at least one value associatedwith the residual portion of the at least one sensor signal.

In further example implementations, one or more principal components ofthe at least one sensor signal may be ascertained based at least in parton the series of samples of the at least one sensor signal. As such, theascertaining of a residual portion may further include ascertaining theresidual portion of the at least one sensor signal based at least inpart on the ascertained one or more principal components. And, thedetermining of the at least one value associated with the residualportion may further include estimating a characteristic of random noiseexpressed in a subspace associated with the residual portion of the atleast one sensor signal, with the characteristic comprising one or morevalues descriptive of how data are distributed with respect to anaverage of the data.

FIGS. 13A and 13B depict graphical diagrams 1300 and 1350 thatillustrate example comparisons between sensor signal values and measuredblood glucose values in relation to non-physiological anomalies forfirst and second sensors, respectively, in accordance with anembodiment. As illustrated, graphical diagrams 1300 (e.g., graphs 1302and 1304) correspond to a first sensor. Graphical diagrams 1350 (e.g.,graphs 1352 and 1354) correspond to a second sensor.

To develop data for graphical diagrams 1300 and 1350, retrospectivesensor fault analysis was performed on data obtained from a closed-loopclinical experiment. Two sensors were inserted on a type 1 diabeticsubject, and data was collected for 36 hours. Sensor current (isig) isplotted along with interpolated blood glucose (BG) concentrationobtained from a glucose analyzer (also known as YSI).

As shown, along the abscissa axis of all four graphs 1302, 1304, 1352,and 1354, time (minutes) is depicted extending from 200 to 2000. Graphs1302 and 1352 depict isig (nAmps) from 0 to 40 along a left ordinateaxis and depict BG (mg/dL) from 0 to 300 along a right ordinate axis.Graphs 1304 and 1354 depict Q statistics from 0 to 3 and from 0 to 2,respectively, along an ordinate axis. A dashed line runs horizontallyalong graphs 1304 and 1354 at Q=1 (e.g., an example of Q_(TH)).

In graphs 1302 and 1352, solid lines represent current sensor signalvalues (isig), and dashed lines represent measured blood glucose (BG).In graphs 1304 and 1354, solid lines represent values for Q statistics.Circles or dots in graphs 1302 and 1352 indicate time-points whenQ-statistics exceed a predetermined threshold value (e.g., Q_(TH)=1). Bycomparing times having relatively higher Q statistical values (e.g.,above the dashed line at Q_(TH)=1) in graphs 1304 and 1354 to the solidlines of graphs 1302 and 1352, respectively, it is apparent that higherQ values correspond to times when the solid lines deviate more rapidlywith respect to the dashed lines due to impurities in the sensor signal.It also appears that sensor 1 (of graphical diagrams 1300) was noisierthan sensor 2 (of graphical diagrams 1350) during the closed-loopclinical experiment.

FIG. 14 is a schematic diagram of an example responsiveness detector1010 that may include a sensor signal stability analyzer 1404 inaccordance with an embodiment. As illustrated, responsiveness detector1010 may include or have access to a series of samples 1004, anunderlying trend metric determiner 1402, a sensor signal stabilityanalyzer 1404, and an alert generator 1406. Underlying trend metricdeterminer 1402 may estimate an underlying trend metric 1408. Sensorsignal stability analyzer 1404 may include at least one stabilitythreshold 1410.

For certain example embodiments, series of samples 1004 may be providedto underlying trend metric determiner 1402. Series of samples 1004 maybe obtained from at least one sensor signal (e.g., as shown in FIGS. 9and 10), and the at least one sensor signal may be acquired from one ormore subcutaneous glucose sensors (e.g., as shown in FIG. 9).

An underlying trend metric determiner 1402 may determine (e.g.,calculate, estimate, ascertain, combinations thereof, etc.) at least onemetric assessing an underlying trend (e.g., underlying trend metric1408) based at least in part on series of samples 1004 to identify achange in responsiveness of at least one sensor signal to blood glucoselevels of a patient over time. Underlying trend metric 1408 may beprovided to sensor signal stability analyzer 1404 (e.g., from underlyingtrend metric determiner 1402).

In example embodiments, an underlying trend metric 1408 may reflectwhether and/or an extent to which a sensor signal is affected by anunstable sensor, such as a sensor that has a changing responsiveness toblood glucose levels of a patient over time. For instance, a glucosesensor may diverge from sensing an accurate glucose level over time(e.g., that diverges upward or downward due to drift). By way of examplebut not limitation, an underlying trend metric 1408 may be related to afundamental, long-term, overall, etc. trend of sensor data and/or valuessampled from such sensor data. For example, a metric assessing anunderlying trend may comprise a monotonic curve derived from sampleddata, an iteratively grown trend value, combinations thereof, and soforth, just to name a couple of examples. As another example, a metricassessing an underlying trend may comprise a slope of a linearregression applied to sampled data, a slope of a linear regressionapplied to a monotonic curve, some combination thereof, and so forth,just to name a couple of examples. However, these are merely examples ofa metric assessing an underlying trend, and claimed subject matter isnot limited in these respects.

A sensor signal stability analyzer 1404 may perform at least onestability assessment with respect to at least one sensor signal based atleast in part on a metric assessing an underlying trend (e.g.,underlying trend metric 1408). Such a stability assessment may compriseat least one comparison of an underlying trend metric 1408 with one ormore stability thresholds 1410 (e.g., at least one predeterminedthreshold). By way of example only, a stability assessment may includecomparing at least one metric assessing an underlying trend with atleast a first predetermined threshold and a second predeterminedthreshold.

In example implementations including first and second predeterminedthresholds, performance of a stability assessment may include assessinga reliability of at least a sensor signal as being in a first state(e.g., a stable state), a second state (e.g., an unstable and driftingstate), or a third state (e.g., an unstable and dying state). Forexample, a reliability of at least one sensor signal may be assessed tobe in a first state responsive to a comparison of at least one metricassessing an underlying trend with a first predetermined threshold. Areliability of at least one sensor signal may be assessed to be in asecond state responsive to a comparison of at least one metric assessingan underlying trend with a first predetermined threshold and a secondpredetermined threshold. A reliability of at least one sensor signal maybe assessed to be in a third state responsive to a comparison of atleast one metric assessing an underlying trend with a secondpredetermined threshold.

If a responsiveness of a sensor signal is assessed to be changing, thensensor signal stability analyzer 1404 may cause alert generator 1406 toissue an alert signal 1006. In an alternative implementation,non-physiological anomaly detector 1008 and responsiveness detector 1010may share an alert generator (e.g., alert generator 1106 (of FIG. 11)and alert generator 1406 may comprise a single alert generator).

FIG. 15 is a flow diagram 1500 of an example method for handlingapparent changes in responsiveness of a glucose sensor signal to bloodglucose levels in a patient in accordance with an embodiment. Asillustrated, flow diagram 1500 may include five operational blocks1502-1510. Although operations 1502-1510 are shown and described in aparticular order, it should be understood that methods may be performedin alternative orders and/or manners (including with a different numberof operations) without departing from claimed subject matter. At leastsome operation(s) of flow diagram 1500 may be performed so as to befully or partially overlapping with other operation(s). Additionally,although the description below may reference particular aspects andfeatures illustrated in certain other figures, methods may be performedwith other aspects and/or features.

For certain example implementations, at operation 1502, a series ofsamples of at least one sensor signal that is responsive to a bloodglucose level of a patient may be obtained. At operation 1504, at leastone metric assessing an underlying trend may be determined, based atleast in part on the series of samples of the at least one sensorsignal, to identify whether the at least one sensor signal appears ischanging a responsiveness to the blood glucose level of the patient overtime.

At operation 1506, a reliability of the at least one sensor signal torespond to the blood glucose level of the patient may be assessed basedat least partly on the at least one metric assessing an underlyingtrend. For example, a comparison of the at least one metric assessing anunderlying trend with at least one predetermined threshold may beperformed. At operation 1508, an alert signal may be generatedresponsive to a comparison of the at least one metric assessing anunderlying trend with at least one predetermined threshold.

At operation 1510, an insulin infusion treatment for the patient may bealtered responsive at least partly to the assessed reliability of the atleast one sensor signal. For example, an insulin infusion treatment fora patient may be altered by changing (e.g., increasing or decreasing) anamount of insulin being infused, by ceasing an infusion of insulin, bydelaying infusion until more samples are taken, by switching to adifferent sensor, by switching to a manual mode, by changing a relativeweighting applied to a given sensor or sensors and/or the samplesacquired there from, any combination thereof, and so forth, just to namea few examples.

In certain example implementations, a subcutaneous glucose sensor maymeasure the glucose level in body fluid. An electro-chemical glucosesensor may generate current at a nanoAmp level. An amplitude of suchcurrent may change based on a glucose level in the body fluid; hence,glucose measurement may be performed. Glucose sensors may be designed tostay in a body for, for example, several days. Unfortunately, a signalprovided from some sensors may gradually drift down (or up) (e.g., acurrent level may gradually drift higher or lower), and such a signalmay eventually die out due to sensor defects, environmental factors, orother issues. Sensor fault detection may therefore involve determiningwhether a signal from a sensor has become unreliable due to a driftingof the signal, such that the signal increasingly diverges further fromactual physiological activity of a patient's blood glucose level.

FIG. 16A depicts a graphical diagram 1600 that illustrates an example ofa downward drifting sensor signal along with physiological activity inaccordance with an embodiment. Because an overall sensor signal from asensor is drifting downward while a blood glucose level is not, aresponse to physiological activity by the sensor may be considered to beunstable and/or dying. The sensor signal appears to be diverging from anactual blood glucose level to an increasing extent as time elapses.

For certain example implementations, detection of such diverging (e.g.,drifting) of a sensor signal may include two phases. A first phase mayinclude trend estimation in which an underlying signal trend (e.g., afundamental, overall, long-term, etc. trend) of a sensor signal isdetermined. A second phase may include performing an assessment (e.g., astability analysis) to determine whether an estimated underlying trendindicates drifting of the sensor signal.

Any one or more of multiple different approaches may be implemented toestimate an underlying signal trend. Three example implementationapproaches for trend estimation are described below: empirical modedecomposition, wavelet decomposition, and iterative trend estimation.With an example implementation of empirical mode decomposition, at leastone metric assessing an underlying trend may be determined bydecomposing at least one sensor signal as represented by a series ofsamples using spline functions to remove relatively higher frequencycomponents from the at least one sensor signal. With an exampleimplementation of wavelet decomposition, at least one metric assessingan underlying trend may be determined by decomposing at least one sensorsignal as represented by a series of samples using at least one discretewavelet transform and reconstructing a smoothed signal from one or moreapproximation coefficients resulting from the at least one discretewavelet transform. With an example implementation of iterative trendestimation, at least one metric assessing an underlying trend may bedetermined by iteratively updating a trend estimation at multiplesamples of a series of samples of at least one sensor signal based atleast partly on a trend estimation at a previous sample and a growthterm.

First, an example of empirical mode decomposition (EMD) is described.EMD may be based on an initial part of a Hilbert-Huang Transform (HHT).HHT is designed to perform “instantaneous” frequency estimation fornonlinear, non-stationary signals. EMD may be used for signaldecomposition in HHT. In EMD, spline functions may be used to graduallyremove details from an original signal. Such a procedure may be repeateduntil a monotonic curve or a curve with but one extreme value remains.Such a monotonic (e.g., smooth) curve may be considered an example of anestimation of an underlying trend and/or underlying trend metric for asignal. A linear regression may be performed on a monotonic curve. Aslope of such a linear regression may represent a quantitativemeasurement of a signal trend (Tr) of a sensor signal and may beconsidered an example of an estimated underlying trend metric.

Second, an example of wavelet decomposition is described. In waveletdecomposition, a discrete wavelet transform (DWT) may be used todecompose a signal into different levels of details. A detail levelhaving a smoothest signal may be considered an approximation signal,which can be reconstructed from approximation coefficients calculatedfrom a DWT. A smooth signal that is reconstructed from approximationcoefficients may be considered an example of an estimation of anunderlying trend and/or underlying trend metric for a signal. A linearregression may be performed on an approximation signal. A slope of sucha linear regression may represent a quantitative measurement of a signaltrend (Tr) of a sensor signal and may be considered an example of anestimated underlying trend metric.

FIGS. 16B and 16C depict graphical diagrams 1630 and 1660, respectively,that illustrate multiple example glucose signals and correspondingmonotonic fundamental signal trends as generated by first and secondexample signal trend analysis approaches, respectively, in accordancewith an embodiment. Graphical diagrams 1630 correspond to an example EMDapproach, and graphical diagrams 1660 correspond to an example waveletdecomposition approach.

Graphs 1632 a, 1634 a, and 1636 a and graphs 1662 a, 1664 a, and 1666 adepict example signals from a glucose sensor. Graphs 1632 b, 1634 b, and1636 b depict example respective corresponding monotonic fundamentalsignal trends generated by an example EMD approach. Graphs 1662 b, 1664b, and 1666 b depict example respective corresponding monotonicfundamental signal trends generated by an example wavelet decompositionapproach via smoothed signals that are reconstructed from approximationcoefficients.

In example implementations, at least one metric assessing an underlyingtrend may be determined by producing the at least one metric assessingan underlying trend using a slope of a linear regression that is derivedat least partly from a series of samples of the at least one sensorsignal. In further example implementations, a series of samples of atleast one sensor signal may be transformed to derive a monotonic curve,and production of at least one metric assessing an underlying trend mayinclude calculating a slope of a linear regression, with the linearregression being derived at least partly from the monotonic curve.

Third, an example of iterative trend estimation is described. Initerative trend estimation, a trend at each signal sample n may beiteratively calculated based on a trend at a previous signal sample n−1.An initial trend can be estimated by linear regression. A slope of alinear regression may be considered as an initial trend Tr(0). Anintercept of a linear regression may be considered as initial growthGr(0). A trend at each point may be estimated as follows using equation(9):Tr(n)=Tr(n−1)+Wg×Gr(n−1).  (9)

In equation (9), Gr(n) may be considered a growth term, and Wg may beconsidered a growth parameter, which can be determined empirically.Growth term Gr(n) may be iteratively updated as well, as shown byequation (10):Gr(n)=Wg×Gr(n−1)+Wt×[sig(n)−Tr(n)],  (10)where Wt may be considered a trend parameter, which can be determinedempirically.

Example approaches for a first phase to estimate an underlying signaltrend are described above with regard to EMD, wavelet decomposition, anditerative trend estimation. Example approaches for a second phase todetermine whether an estimated underlying trend indicates drifting of asensor signal are described below.

For an example second phase, at least one assessment may be performed todecide whether a determined trend Tr(n) at signal sample n indicates achanging responsiveness of a sensor signal to blood glucose levels of apatient (e.g., a drifting of the sensor signal). Such a trend value maybe determined using any one or more of the above-three described exampleimplementations and/or an alternative approach.

In an example implementation for a second phase, two positive stabilitythresholds T1 and T2 (e.g., a first and a second predeterminedthreshold) may be used for drift detection, where T1<T2, to establishthree example detection categories: normal operation, drifting, anddying. However, one stability threshold to determine an affirmative ornegative drifting decision may alternatively be implemented withoutdeparting from claimed subject matter. If an absolute value of trendTr(n) is less than T1, a sensor trend may be deemed to be within normalfluctuations. Thus, no drifting may be declared in such circumstances,and/or a sensor may be considered stable. In such circumstances, adrifting factor F may be set, by way of example only, to zero (0).

If an absolute value of trend Tr(n) is between T1 and T2, a sensor trendmay be deemed to be outside of normal fluctuations, and/or a sensor maybe considered to be unstable and drifting. Hence, drifting may bedeclared. A severity of such drifting may be measured by a driftingfactor F as shown, by way of example only, in equation (11):

$\begin{matrix}{F = {\frac{{{abs}\left\lbrack {{Tr}(n)} \right\rbrack} - {T\; 1}}{{T\; 2} - {T\; 2}}.}} & (11)\end{matrix}$Drifting factor F may be set to have a value range between 0 and 1. Thelarger a drifting factor F value, the more severe a drifting may beconsidered to be. However, drifting factor(s) may be calculated inalternative manners without departing from claimed subject matter. In anexample implementation, at least one value indicating a severity ofdivergence by at least one sensor signal from a blood glucose level of apatient over time may be ascertained based at least partly on at leastone metric assessing an underlying trend, a first predeterminedthreshold, and a second predetermined threshold. Also, if an absolutevalue of trend Tr(n) is greater than T2, a sensor may be consideredunstable and may be declared to be dying due to severe drifting. In suchcircumstances, a drifting factor F may be set, by way of example only,to one (1).

FIG. 17 is a schematic diagram 1700 of an example controller 12 thatproduces output information 1712 based on input data 1710 in accordancewith an embodiment. As illustrated, controller 12 may include one ormore processors 1702 and at least one memory 1704. In certain exampleembodiments, memory 1704 may store or otherwise include instructions1706 and/or sensor sample data 1708. Sensor sample data 1708 mayinclude, by way of example but not limitation, blood glucose sensormeasurements, such as series of samples 1004 (e.g. of FIGS. 10, 11, and14).

In particular example implementations, controller 12 of FIG. 17 maycorrespond to a controller 12 of FIGS. 1, 9, and/or 10. Input data 1710may include, for example, sensor measurements (e.g., from an ISF currentsensor). Output information 1712 may include, for example, one or morecommands, and such commands may include reporting information. Currentsensor measurements of input data 1710 may correspond to sensor signal16 (e.g., of FIGS. 1, 9, and 10) and/or sampled values resulting therefrom. Commands of output information 1712 may correspond to commands 22(e.g., of FIGS. 1, 9, and 10), which may be derived from one or morealert signals 1006 (e.g., of FIGS. 10, 11, and 14) and/or instructionsor other information resulting there from.

In certain example embodiments, input data 1710 may be provided tocontroller 12. Based on input data 1710, controller 12 may produceoutput information 1712. Current sensor measurements that are receivedas input data 1710 may be stored as sensor sample data 1708. Controller12 may be programmed with instructions 1706 to perform algorithms,functions, methods, etc.; to implement attributes, features, etc.; andso forth that are described herein. For example, a controller 12 may beconfigured to perform the functions described herein with regard to anon-physiological anomaly detector 1008 and/or a responsiveness detector1010 (e.g., of FIGS. 10, 11, and/or 14). Controller 12 may therefore becoupled to at least one blood glucose sensor to receive one or moresignals based on blood glucose sensor measurements.

A controller 12 that comprises one or more processors 1702 may executeinstructions 1706 to thereby render a controller unit a special purposecomputing device to perform algorithms, functions, methods, etc.; toimplement attributes, features, etc.; and so forth that are describedherein. Processor(s) 1702 may be realized as microprocessors, digitalsignal processors (DSPs), application specific integrated circuits(ASICs), programmable logic devices (PLDs), controllers,micro-controllers, a combination thereof, and so forth, just to name afew examples. Alternatively, an article may comprise at least onestorage medium (e.g., such as one or more memories) having storedthereon instructions 1706 that are executable by one or more processors.

Unless specifically stated otherwise, as is apparent from the precedingdiscussion, it is to be appreciated that throughout this specificationdiscussions utilizing terms such as “processing”, “computing”,“calculating”, “determining”, “assessing”, “estimating”, “identifying”,“obtaining”, “representing”, “receiving”, “transmitting”, “storing”,“analyzing”, “measuring”, “detecting”, “controlling”, “delaying”,“initiating”, “providing”, “performing”, “generating”, “altering” and soforth may refer to actions, processes, etc. that may be partially orfully performed by a specific apparatus, such as a special purposecomputer, special purpose computing apparatus, a similar special purposeelectronic computing device, and so forth, just to name a few examples.In the context of this specification, therefore, a special purposecomputer or a similar special purpose electronic computing device may becapable of manipulating or transforming signals, which are typicallyrepresented as physical electronic and/or magnetic quantities withinmemories, registers, or other information storage devices; transmissiondevices; display devices of a special purpose computer; or similarspecial purpose electronic computing device; and so forth, just to namea few examples. In particular example embodiments, such a specialpurpose computer or similar may comprise one or more processorsprogrammed with instructions to perform one or more specific functions.Accordingly, a special purpose computer may refer to a system or adevice that includes an ability to process or store data in the form ofsignals. Further, unless specifically stated otherwise, a process ormethod as described herein, with reference to flow diagrams orotherwise, may also be executed or controlled, in whole or in part, by aspecial purpose computer.

It should be understood that aspects described above are examples onlyand that embodiments may differ there from without departing fromclaimed subject matter. Also, it should be noted that although aspectsof the above systems, methods, apparatuses, devices, processes, etc.have been described in particular orders and in particular arrangements,such specific orders and arrangements are merely examples and claimedsubject matter is not limited to the orders and arrangements asdescribed. It should additionally be noted that systems, devices,methods, apparatuses, processes, etc. described herein may be capable ofbeing performed by one or more computing platforms.

In addition, instructions that are adapted to realize methods,processes, etc. that are described herein may be capable of being storedon a storage medium as one or more machine readable instructions. Ifexecuted, machine readable instructions may enable a computing platformto perform one or more actions. “Storage medium” as referred to hereinmay relate to media capable of storing information or instructions whichmay be operated on, or executed by, one or more machines (e.g., thatinclude at least one processor). For example, a storage medium maycomprise one or more storage articles and/or devices for storingmachine-readable instructions or information. Such storage articlesand/or devices may comprise any one of several media types including,for example, magnetic, optical, semiconductor, a combination thereof,etc. storage media. By way of further example, one or more computingplatforms may be adapted to perform one or more processes, methods, etc.in accordance with claimed subject matter, such as methods, processes,etc. that are described herein. However, these are merely examplesrelating to a storage medium and a computing platform and claimedsubject matter is not limited in these respects.

Although there have been illustrated and described what are presentlyconsidered to be example features, it will be understood by thoseskilled in the art that various other modifications may be made, andequivalents may be substituted, without departing from claimed subjectmatter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from central concepts that are described herein. Therefore, itis intended that claimed subject matter not be limited to particularexamples disclosed, but that such claimed subject matter may alsoinclude all aspects falling within the scope of appended claims, andequivalents thereof.

What is claimed is:
 1. A method comprising: obtaining a series of samples of at least one sensor signal is responsive to a blood glucose level of a patient; determining, based at least partly on the series of samples, at least one metric assessing an underlying trend of a change in responsiveness of the at least one sensor signal to the blood glucose level of the patient over time, wherein said determining comprises: iteratively updating a trend estimation at multiple samples of the series of samples of the at least one sensor signal based at least partly on a trend estimation at a previous sample and a growth term; and assessing a reliability of the at least one sensor signal to respond to the blood glucose level of the patient based at least partly on the at least one metric assessing an underlying trend.
 2. The method of claim 1, further comprising: generating an alert signal responsive to a comparison of the at least one metric assessing an underlying trend with at least one predetermined threshold.
 3. The method of claim 1, wherein said assessing comprises: comparing the at least one metric assessing an underlying trend with at least a first predetermined threshold and a second predetermined threshold.
 4. The method of claim 1, further comprising: acquiring the at least one sensor signal from one or more subcutaneous glucose sensors, wherein the at least one metric assessing an underlying trend reflects an apparent reliability of the at least one sensor signal that is acquired from the one or more subcutaneous glucose sensors.
 5. The method of claim 1, further comprising: altering an insulin infusion treatment for the patient responsive at least partly to the assessed reliability of the at least one sensor signal.
 6. The method of claim 1, wherein said determining comprises: producing the at least one metric assessing an underlying trend using a slope of a linear regression that is derived at least partly from the series of samples of the at least one sensor signal.
 7. An apparatus comprising: a controller to obtain a series of samples of at least one sensor signal that is responsive to a blood glucose level of a patient, said controller comprising one or more processors to: determine, based at least partly on the series of samples, at least one metric assessing an underlying trend of a change in responsiveness of the at least one sensor signal to the blood glucose level of the patient over time; and assess a reliability of the at least one sensor signal to respond to the blood glucose level of the patient based at least partly on the at least one metric assessing an underlying trend; wherein said controller is capable of assessing by: comparing the at least one metric assessing an underlying trend with at least a first predetermined threshold and a second predetermined threshold; and ascertaining at least one value indicating a severity of divergence by the at least one sensor signal from the blood glucose level of the patient over time based at least partly on the at least one metric assessing an underlying trend, the first predetermined threshold, and the second predetermined threshold.
 8. The apparatus of claim 7, wherein said One or more processors of said controller are further to: generate an alert signal responsive to a comparison of the at least one metric assessing an underlying trend with at least one predetermined threshold.
 9. The apparatus of claim 7, wherein said one or more processors of said controller are further to: acquire the at least one sensor signal from one or more subcutaneous glucose sensors, wherein the at least one metric assessing an underlying trend reflects an apparent reliability of the at least one sensor signal that is acquired from the one or more subcutaneous glucose sensors.
 10. The apparatus of claim 7, wherein said one or more processors of said controller are further to: alter an insulin infusion treatment for the patient responsive at least partly to the assessed reliability of the at least one sensor signal.
 11. The apparatus of claim 7, wherein said controller is capable of determining by: producing the at least one metric assessing an underlying trend using a slope of a linear regression that is derived at least partly from the series of samples of the at least one sensor signal.
 12. The apparatus of claim 11, wherein said one or more processors of said controller are further to: transform the series of samples of the at least one sensor signal to derive a monotonic curve, wherein said controller is capable of producing the at least one metric assessing an underlying trend by calculating the slope of the linear regression, the linear regression being derived at least partly from the monotonic curve.
 13. The apparatus of claim 7, wherein said controller is capable of determining by: decomposing the at least one sensor signal as represented by the series of samples using at least one empirical mode decomposition and one or more spline functions to remove relatively higher frequency components from the at least one sensor signal.
 14. The apparatus of claim 7, wherein said controller is capable of determining by: decomposing the at least one sensor signal as represented by the series of samples using at least one discrete wavelet transform; and reconstructing a smoothed signal from one or more approximation coefficients resulting from the at least one discrete wavelet transform.
 15. The apparatus of claim 7, wherein said controller is capable of determining by: iteratively updating a trend estimation at multiple samples of the series of samples of the at least one sensor signal based at least partly on a trend estimation at a previous sample and a growth term.
 16. An article comprising: at least one storage medium having stored thereon instructions executable by one or more processors to: obtain a series of samples of at east one sensor signal that is responsive to a blood glucose level of a patient; determine, based at least partly on the series of samples, at least one metric assessing an underlying trend of a change in responsiveness of the at least one sensor signal to the blood glucose level of the patient over time; and assess a reliability of the at least one sensor signal to respond to the blood glucose level of the patient based at least partly on the at least one metric assessing an underlying trend; wherein to determine comprises to: decompose the at least one sensor signal as represented by the series of samples using at least one empirical mode decomposition and one or more spline functions to remove relatively higher frequency components from the at least one sensor signal.
 17. The article of claim 16, wherein said instructions are further executable by said one or more processors to generate an alert signal responsive to a comparison of the at least one metric assessing an underlying trend with at least one predetermined threshold. 